{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:47:38Z","timestamp":1776811658134,"version":"3.51.2"},"reference-count":19,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,4,4]]},"abstract":"<jats:p>New network attack platforms such as personal to personal botnets pose a great threat to cyberspace, but there is no corresponding detection method to detect them. In order to improve the security of topological networks, this research designs a mathematical modeling analysis method for potential attack detection based on convolutional neural networks. This method determines the potential attack risk assessment function through the feature extraction of vulnerable areas in network topology and the probability model of potential attacks, and then detects potential attacks by means of convolutional neural network data modeling. The experimental results show that the false detection rate and missed detection rate of the three methods for potential attacks are lower than 9% and 8% respectively, but the false detection rate and missed detection rate of the method given in the study are the lowest, and can always be kept below 5%. At the same time, the detection time of potential attacks of this method is shorter than that of the other two detection methods. The detection of potential attacks provides a technical guarantee for the safe operation of the network.<\/jats:p>","DOI":"10.3233\/jcm-226586","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T11:24:29Z","timestamp":1669721069000},"page":"1101-1113","source":"Crossref","is-referenced-by-count":0,"title":["Mathematical modeling analysis of potential attack detection in topology network based on convolutional neural network"],"prefix":"10.66113","volume":"23","author":[{"given":"Jie","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"12","key":"10.3233\/JCM-226586_ref1","first-page":"455","article-title":"Real-time detection of potential multi-step attacks on multi-operating system topology networks","volume":"37","author":"Xiao","journal-title":"Comput Simul."},{"issue":"5","key":"10.3233\/JCM-226586_ref3","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1049\/bme2.12053","article-title":"Unknown presentation attack detection against rational attackers","volume":"10","author":"Khodabakhsh","year":"2021","journal-title":"IET Biometrics."},{"issue":"2","key":"10.3233\/JCM-226586_ref4","first-page":"158","article-title":"Machine learning based attacks detection and countermeasures in IoT","volume":"13","author":"Zagrouba","year":"2021","journal-title":"Int J Commun Networks Inf Secur."},{"issue":"1","key":"10.3233\/JCM-226586_ref5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TIFS.2019.2916652","article-title":"Biometric face presentation attack detection with multi-channel convolutional neural network","volume":"15","author":"George","year":"2020","journal-title":"IEEE Trans Inf Forensics Secur."},{"issue":"2","key":"10.3233\/JCM-226586_ref6","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1109\/TII.2020.2974520","article-title":"Deep learning-based DDoS-attack detection for cyber-physical system over 5G network","volume":"17","author":"Hussain","year":"2021","journal-title":"IEEE Trans Ind Inf."},{"issue":"5","key":"10.3233\/JCM-226586_ref7","first-page":"1549","article-title":"Detection of LDoS attacks based on wavelet energy entropy and hidden semi-markov models","volume":"31","author":"Wu","year":"2020","journal-title":"J Software."},{"issue":"1","key":"10.3233\/JCM-226586_ref8","first-page":"35","article-title":"Optimized deep learning-based attack detection framework for secure virtualized infrastructures in cloud","volume":"35","author":"Bhavana","year":"2021","journal-title":"Int J Numer Modell: Electr Networks, Devices Fields."},{"issue":"2","key":"10.3233\/JCM-226586_ref9","doi-asserted-by":"crossref","first-page":"96","DOI":"10.2174\/1872212115666210714143008","article-title":"DDoS attack detection in software defined networks by various metrics","volume":"16","author":"Malallah","year":"2022","journal-title":"Recent Pat Eng."},{"key":"10.3233\/JCM-226586_ref10","doi-asserted-by":"crossref","first-page":"102661","DOI":"10.1016\/j.adhoc.2021.102661","article-title":"Deep learning-based reliable routing attack detection mechanism for industrial Internet of Things","volume":"123","author":"Sharmistha","year":"2021","journal-title":"Ad Hoc Networks."},{"issue":"3","key":"10.3233\/JCM-226586_ref11","first-page":"584","article-title":"Web attack detection method based on convolutional neural network","volume":"40","author":"Tian","year":"2019","journal-title":"J Chin Comput Syst."},{"issue":"1","key":"10.3233\/JCM-226586_ref12","first-page":"84","article-title":"Detection of network attack based on convolutional neural network","volume":"26","author":"Qin","year":"2019","journal-title":"J Lanzhou Inst Technol."},{"issue":"1","key":"10.3233\/JCM-226586_ref13","first-page":"38","article-title":"Stain attacks and defenses against convolutional neural networks","volume":"32","author":"Hu","year":"2020","journal-title":"J Zhejiang Univ Sci Technol."},{"key":"10.3233\/JCM-226586_ref14","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/s41315-022-00227-1","article-title":"IoT authentication model with optimized deep Q network for attack detection and mitigation","volume":"6","author":"Palekar","year":"2022","journal-title":"Int J Intell Rob Appl."},{"key":"10.3233\/JCM-226586_ref15","doi-asserted-by":"crossref","unstructured":"Htwe CS, Thant YM, Thwin M. Botnets attack detection using machine learning approach for IoT environment. 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